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Analysts and corporate liquidity policy

Description

This paper examines how equity analysts' roles as information intermediaries and monitors affect corporate liquidity policy and its associated value of cash, providing new evidence that analysts have a direct impact on corporate liquidity policy. Greater analyst coverage (1) reduces information asymmetry between a firm and outside shareholders and (2) enhances the monitoring process. Consistent with these arguments, analyst coverage increases the value of cash, thereby allowing firms to hold more cash. The cash-to-assets ratio increases by 5.2 percentage points when moving from the bottom analyst-coverage decile to the top decile. The marginal value of $1 of corporate cash holdings is $0.93 for the bottom analyst-coverage decile and $1.83 for the top decile. The positive effects remain robust after a battery of endogeneity checks. I also perform tests employing a unique dataset that consists of public and private firms, as well as a dataset that consists of public firms that have gone private. A public firm with analyst coverage can hold approximately 8% more cash than its private counterpart. These findings constitute new evidence on the real effect of analyst coverage.

ContributorsChang, Ching-Hung (Author) / Bates, Thomas (Thesis advisor) / Bharath, Sreedhar (Committee member) / Lindsey, Laura (Committee member) / Arizona State University (Publisher)

Created2012

Analyzing the effects of Bollinger bands on the probability of stock options using support vector machines

Description

The purpose of this research is to efficiently analyze certain data provided and to see if a useful trend can be observed as a result. This trend can be used to analyze certain probabilities. There are three main pieces of data which are being analyzed in this research: The value for δ of the call and put option, the %B value of the stock, and the amount of time until expiration of the stock option. The %B value is the most important. The purpose of analyzing the data is to see the relationship between the variables and, given certain values, what is the probability the trade makes money. This result will be used in finding the probability certain trades make money over a period of time.

Since options are so dependent on probability, this research specifically analyzes stock options rather than stocks themselves. Stock options have value like stocks except options are leveraged. The most common model used to calculate the value of an option is the Black-Scholes Model [1]. There are five main variables the Black-Scholes Model uses to calculate the overall value of an option. These variables are θ, δ, γ, v, and ρ. The variable, θ is the rate of change in price of the option due to time decay, δ is the rate of change of the option’s price due to the stock’s changing value, γ is the rate of change of δ, v represents the rate of change of the value of the option in relation to the stock’s volatility, and ρ represents the rate of change in value of the option in relation to the interest rate [2]. In this research, the %B value of the stock is analyzed along with the time until expiration of the option. All options have the same δ. This is due to the fact that all the options analyzed in this experiment are less than two months from expiration and the value of δ reveals how far in or out of the money an option is.

The machine learning technique used to analyze the data and the probability

is support vector machines. Support vector machines analyze data that can be classified in one of two or more groups and attempts to find a pattern in the data to develop a model, which reliably classifies similar, future data into the correct group. This is used to analyze the outcome of stock options.

ContributorsReeves, Michael (Author) / Richa, Andrea (Thesis advisor) / McCarville, Daniel R. (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)

Created2015

From Playground to Boardroom: Endowed Social Status and Managerial Performance

Description

By matching a CEO's place of residence in his or her formative years with U.S. Census survey data, I obtain an estimate of the CEO's family wealth and study the link between the CEO's endowed social status and firm performance. I find that, on average, CEOs born into poor families outperform those born into wealthy families, as measured by a variety of proxies for firm performance. There is no evidence of higher risk-taking by the CEOs from low social status backgrounds. Further, CEOs from less privileged families perform better in firms with high R&D spending but they underperform CEOs from wealthy families when firms operate in a more uncertain environment. Taken together, my results show that endowed family wealth of a CEO is useful in identifying his or her managerial ability.

ContributorsDu, Fangfang (Author) / Babenko, Ilona (Thesis advisor) / Bates, Thomas (Thesis advisor) / Tserlukevich, Yuri (Committee member) / Wang, Jessie (Committee member) / Arizona State University (Publisher)

Created2018